Jianlong Li

Also published as: 李, 剑龙

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2023

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CCL23-Eval 任务1系统报告:基于增量预训练与对抗学习的古籍命名实体识别(System Report for CCL23-Eval Task 1:::GuNER Based on Incremental Pretraining and Adversarial Learning)
Jianlong Li (剑龙李,) | Youren Yu (于右任) | Xueyang Liu (刘雪阳) | Siwen Zhu (朱思文)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)

“古籍命名实体识别是正确分析处理古汉语文本的基础步骤,也是深度挖掘、组织人文知识的重要前提。古汉语信息熵高、艰涩难懂,因此该领域技术研究进展缓慢。针对现有实体识别模型抗干扰能力差、实体边界识别不准确的问题,本文提出使用NEZHA-TCN与全局指针相结合的方式进行古籍命名实体识别。同时构建了一套古文数据集,该数据集包含正史中各种古籍文本,共87M,397,995条文本,用于NEZHA-TCN模型的增量预训练。在模型训练过程中,为了增强模型的抗干扰能力,引入快速梯度法对词嵌入层添加干扰。实验结果表明,本文提出的方法能够有效挖掘潜藏在古籍文本中的实体信息,F1值为95.34%。”
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